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Face verification based on deep Bayesian convolutional neural network in unconstrained environment

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Abstract

Unconstrained face verification aims to verify whether two specify images contain the same person. In this paper, we propose a deep Bayesian convolutional neural network (DBCNN) framework to extract facial features and measure their similarity for face verification in unconstrained conditions. Specifically, we design a deep convolutional neural network and construct a Bayesian probabilistic model by transferring the Bayesian likelihood ratio function into linear decision function. By training a decision line rather than finding a suitable threshold, we further enlarge the distances between inter-class and intra-class in unconstrained environment. Finally, we comprehensively evaluate our method on LFW, CACD-VS and MegaFace datasets. The test results on LFW and CACD-VS datasets show that our method can shrink intra-class variations significantly. The performance of our DBCNN model on MegaFace dataset proves that our model can achieve comparable performance to state-of-the-art methods on face verification with relative small training data and only one single network.

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Acknowledgements

We thank the reviewers and the editor for their valuable comments. This work has been supported by the National Natural Science Foundation of China (Nos. 61772387 and 61372068), the Research Fund for the Doctoral Program of Higher Education of China (No. 20130203110005) and also supported by the ISN State Key Laboratory.

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  1. The State Key Laboratory of Integrated Services Networks, Xidian University, 710071, China

    Mengjie Zhao, Bin Song, Yue Zhang & Hao Qin

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  1. Mengjie Zhao
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  2. Bin Song
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  3. Yue Zhang
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  4. Hao Qin
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Correspondence to Bin Song.

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Zhao, M., Song, B., Zhang, Y. et al. Face verification based on deep Bayesian convolutional neural network in unconstrained environment. SIViP 12, 819–826 (2018). https://doi.org/10.1007/s11760-017-1223-3

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  • DOI: https://doi.org/10.1007/s11760-017-1223-3

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